A Mathematical Concepts Enhanced Annotation Model for Chinese Arithmetic Word Problems
Xiaopan Lyu, Xiaoqian Liu, Rao Peng, Chuanzhi Yang, Xinguo Yu · 2023
Word annotation has been adopted as a crucial preprocessing step in developing algorithms for solving arithmetic word problems (AWPs). This paper proposes a mathematical concepts enhanced annotation (MONA) model for Chinese arithmetic word problems (AWPs), consisting of two modules: mathematical Chinese word segmentation and mathematical part-of-speech tagging. The MONA model utilizes mathematical concepts to enhance the training tasks of both modules, improving their capabilities to ensure high-quality AWPs annotations. The mathematical Chinese word segmentation module is trained on an AWP corpus segmented based on mathematical concepts, resulting in higher accuracy in identifying mathematical concepts compared to general segmentation models. The mathematical part-of-speech tagging module redefines part-of-speech categories into seven classes according to mathematical concepts and trains the MONA model on the redefined part-of-speech tags, allowing more suitable part-of-speech tagging for acquiring math relations when solving AWPs. Experimental results show the proposed MONA model outperforms baseline models on benchmark AWP datasets.